docs: def sls(minuend, subtrahend, metric = "ssd", noise = "global", signed = True,
          sn_size = None, sn_footprint = None, sn_mode = "reflect", sn_cval = 0.0,
          pn_size = None, pn_footprint = None, pn_mode = "reflect", pn_cval = 0.0):
      r"""
      Computes the signed local similarity between two images.
  
      Compares a patch around each voxel of the minuend array to a number of patches
      centered at the points of a search neighbourhood in the subtrahend. Thus, creates
      a multi-dimensional measure of patch similarity between the minuend and a
      corresponding search area in the subtrahend.
  
      This filter can also be used to compute local self-similarity, obtaining a
      descriptor similar to the one described in [1]_.
  
      Parameters
      ----------
      minuend : array_like
          Input array from which to subtract the subtrahend.
      subtrahend : array_like
          Input array to subtract from the minuend.
      metric : {'ssd', 'mi', 'nmi', 'ncc'}, optional
          The `metric` parameter determines the metric used to compute the
          filter output. Default is 'ssd'.
      noise : {'global', 'local'}, optional
          The `noise` parameter determines how the noise is handled. If set
          to 'global', the variance determining the noise is a scalar, if
          set to 'local', it is a Gaussian smoothed field of estimated local
          noise. Default is 'global'.
      signed : bool, optional
          Whether the filter output should be signed or not. If set to 'False',
          only the absolute values will be returned. Default is 'True'.
      sn_size : scalar or tuple, optional
          See sn_footprint, below
      sn_footprint : array, optional
          The search neighbourhood.
          Either `sn_size` or `sn_footprint` must be defined. `sn_size` gives
          the shape that is taken from the input array, at every element
          position, to define the input to the filter function.
          `sn_footprint` is a boolean array that specifies (implicitly) a
          shape, but also which of the elements within this shape will get
          passed to the filter function. Thus ``sn_size=(n,m)`` is equivalent
          to ``sn_footprint=np.ones((n,m))``. We adjust `sn_size` to the number
          of dimensions of the input array, so that, if the input array is
          shape (10,10,10), and `sn_size` is 2, then the actual size used is
          (2,2,2).
      sn_mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
          The `sn_mode` parameter determines how the array borders are
          handled, where `sn_cval` is the value when mode is equal to
          'constant'. Default is 'reflect'
      sn_cval : scalar, optional
          Value to fill past edges of input if `sn_mode` is 'constant'. Default
          is 0.0
      pn_size : scalar or tuple, optional
          See pn_footprint, below
      pn_footprint : array, optional
          The patch over which the distance measure is applied.
          Either `pn_size` or `pn_footprint` must be defined. `pn_size` gives
          the shape that is taken from the input array, at every element
          position, to define the input to the filter function.
          `pn_footprint` is a boolean array that specifies (implicitly) a
          shape, but also which of the elements within this shape will get
          passed to the filter function. Thus ``pn_size=(n,m)`` is equivalent
          of dimensions of the input array, so that, if the input array is
          shape (10,10,10), and `pn_size` is 2, then the actual size used is
          (2,2,2).
      pn_mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
          The `pn_mode` parameter determines how the array borders are
          handled, where `pn_cval` is the value when mode is equal to
          'constant'. Default is 'reflect'
      pn_cval : scalar, optional
          Value to fill past edges of input if `pn_mode` is 'constant'. Default
          is 0.0
          
      Returns
      -------
      sls : ndarray
          The signed local similarity image between subtrahend and minuend.
  
      References
      ----------
      
      .. [1] Mattias P. Heinrich, Mark Jenkinson, Manav Bhushan, Tahreema Matin, Fergus V. Gleeson, Sir Michael Brady, Julia A. Schnabel
             MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration
             Medical Image Analysis, Volume 16, Issue 7, October 2012, Pages 1423-1435, ISSN 1361-8415
             http://dx.doi.org/10.1016/j.media.2012.05.008
      """
      minuend = numpy.asarray(minuend)
      subtrahend = numpy.asarray(subtrahend)
      
      if numpy.iscomplexobj(minuend):
          raise TypeError('complex type not supported')
      if numpy.iscomplexobj(subtrahend):
          raise TypeError('complex type not supported')  
      
      mshape = [ii for ii in minuend.shape if ii > 0]
      sshape = [ii for ii in subtrahend.shape if ii > 0]
      if not len(mshape) == len(sshape):
          raise RuntimeError("minuend and subtrahend must be of same shape")
      if not numpy.all([sm == ss for sm, ss in zip(mshape, sshape)]):
          raise RuntimeError("minuend and subtrahend must be of same shape")
      
      sn_footprint = __make_footprint(minuend, sn_size, sn_footprint)
      sn_fshape = [ii for ii in sn_footprint.shape if ii > 0]
      if len(sn_fshape) != minuend.ndim:
          raise RuntimeError('search neighbourhood footprint array has incorrect shape.')
      
      #!TODO: Is this required?
      if not sn_footprint.flags.contiguous:
          sn_footprint = sn_footprint.copy()
      
      # created a padded copy of the subtrahend, whereas the padding mode is always 'reflect'  
      subtrahend = pad(subtrahend, footprint=sn_footprint, mode=sn_mode, cval=sn_cval)
      
      # compute slicers for position where the search neighbourhood sn_footprint is TRUE
      slicers = [[slice(x, (x + 1) - d if 0 != (x + 1) - d else None) for x in range(d)] for d in sn_fshape]
      slicers = [sl for sl, tv in zip(itertools.product(*slicers), sn_footprint.flat) if tv]
      
      # compute difference images and sign images for search neighbourhood elements
      ssds = [ssd(minuend, subtrahend[slicer], normalized=True, signed=signed, size=pn_size, footprint=pn_footprint, mode=pn_mode, cval=pn_cval) for slicer in slicers]
      distance = [x[0] for x in ssds]
      distance_sign = [x[1] for x in ssds]
  
      # compute local variance, which constitutes an approximation of local noise, out of patch-distances over the neighbourhood structure
      variance = numpy.average(distance, 0)
      variance = gaussian_filter(variance, sigma=3) #!TODO: Figure out if a fixed sigma is desirable here... I think that yes
      if 'global' == noise:
          variance = variance.sum() / float(numpy.product(variance.shape))
      # variance[variance < variance_global / 10.] = variance_global / 10. #!TODO: Should I keep this i.e. regularizing the variance to be at least 10% of the global one?
      
      # compute sls
      sls = [dist_sign * numpy.exp(-1 * (dist / variance)) for dist_sign, dist in zip(distance_sign, distance)]
      
      # convert into sls image, swapping dimensions to have varying patches in the last dimension
      return numpy.rollaxis(numpy.asarray(sls), 0, minuend.ndim + 1)